1. Field of the Invention
The present invention relates to the technical sphere of the petroleum industry, and more particularly to the development of underground reservoirs such as petroleum reservoirs or gas storage sites. In particular, the invention allows efficient planning of the development of a reservoir by selecting the positions where new wells are to be drilled, for which the production potential will be maximal.
2. Description of the Prior Art
Optimization and development of petroleum reservoirs is based on the most accurate possible description of the structure, namely, the petrophysical properties, the fluid properties, etc., of the reservoir being studied. A tool is used allowing accounting for these aspects in an approximate way which is a reservoir model. The model is a model of the subsoil, representative of both its structure and its behavior. Generally, this type of model is represented in a computer and is referred to as a “numerical model.” A reservoir model comprises a mesh or grid, generally three-dimensional, associated with one or more petrophysical property maps (porosity, permeability, saturation, etc.). The association assigns values of these petrophysical properties to each cell of the grid.
In order to be considered reliable, the reservoir model must meet as much as possible all the data collected in the field which are well-log data measured along wells, measurements performed on rock samples taken in wells, data deduced from seismic acquisition surveys, production data such as oil and water flow rates, pressure data, etc. These data are not sufficient for characterizing precisely the petrophysical property values to be assigned to the cells of the model, which is why a stochastic formalism is generally used. The petrophysical properties are considered as realizations of random functions. A possible image of the reservoir, that is a model, is then generated from geostatistical simulation techniques. Solving flow equations for this model provides production responses. These responses are then compared with the production data measured in the wells. The difference between the simulated responses and the data acquired in the field has to be minimized so as to increase the reservoir model predictivity. This stage involves a calibration or optimization procedure, which is in general consumptive of computation time because it is an iterative process requiring a flow simulation per iteration. Currently, a single flow simulation often requires several hours of computation time. Furthermore, there is not only one reservoir model meeting the production data, but several ones due to the uncertainty on the parameters. One of the dominating factors regarding uncertainties concerning the spatial distribution of the petrophysical properties is geological uncertainty.
When a model meeting the data measured in the field is obtained, it is used to predict the fluid displacements in the reservoir and to plan the future development of the field. For example, for mature fields, it must be possible to select the zones where new wells are to be drilled, either in order to produce oil by depletion drive or to inject a fluid that maintains the pressure at a sufficient level in the reservoir. The performance of a well at a given point can be assessed using the reservoir model by positioning the well in the desired position and carrying out a flow simulation. The performance of a well can be assessed from the amount of hydrocarbons it produces. The final goal being maximization of the production or of the profitability of the field, it should be possible to test all the possible positions and thus to select the best one. Such an approach is inappropriate in practice because it involves too high a computation time. One alternative is launching an optimization procedure intended to have the best location possible for a well to optimize the production. However, this approach remains delicate to implement because it requires several thousand iterations.
The concept of production indicator maps, also referred to as quality maps in the literature, has been introduced in order to deal in a practical manner with the problem of positioning new wells in a reservoir. It is a two-dimensional map comprising a set of cells where each cell is associated with a real value that shows how a new well placed in the cell in question impacts the production or the net present value (NPV) in relation to the base case. The base case corresponds to the initial development scheme. As a scheme for which no new well is added (Da Cruz, P. S., Home, R. N., Deutsch, C., The Quality map: A Tool for Reservoir Quantification and Decision Making, SPE ATCE, SPE 56578, Houston, Tex., USA, 1999).
A production indicator defines an impact on the production of fluid (hydrocarbon) linked with the addition of a well in the cell considered. To construct this map, a flow simulation can be performed for each cell where a well can be positioned. If the reservoir comprises NX and NY cells along axes X and Y, the total number of cells to be examined is NX×NY minus the number of non-active cells and for cells that already have a well for the base case. This approach requires a significant computation time insofar as NX×NY is large.
In order to reduce the computation times, an interpolation approach has been considered (Cottini-Loureiro, A., Araujo, M., Optimized Well Location by Combination of Multiple Realization Approach and Quality Map Methods, SPE 95413, SPE ATCE, Dallas, Tex., USA, 9-12 October, 2005). A simulation is then carried out for some cells of the map, and the values in the other cells are estimated by interpolation.
Moreover, in view of the geological uncertainty being high, several reservoir models meeting the data collected in the field, and not only one model, have to be studied and a production indicator map has to be constructed for each one of these reservoir models. The interpolation techniques used to date for constructing these maps are essentially based on kriging (Chilès, J. P., Delfiner, P., Geostatistics: Modeling Spatial Uncertainty. Wiley, New York, pp. 695, 1999) and they require several flow simulations. If the latter have to be performed for each reservoir model, the total number of flow simulations is significant.